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Keyword extraction for text categorization

An, Jiyuan and Chen, Yi-Ping Phoebe 2005, Keyword extraction for text categorization, in Proceedings of the 2005 International Conference on Active Media Technology, IEEE, Piscataway, N.J., pp. 556-561.

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Title Keyword extraction for text categorization
Author(s) An, Jiyuan
Chen, Yi-Ping Phoebe
Conference name Active Media Technology. Conference (2005: Kagawa, Japan)
Conference location Kagawa, Japan
Conference dates 19-21 May 2005
Title of proceedings Proceedings of the 2005 International Conference on Active Media Technology
Editor(s) Tarumi, H.
Li, Y.
Yoshida, T.
Publication date 2005
Conference series Active Media Technology Conference
Start page 556
End page 561
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Text categorization (TC) is one of the main applications of machine learning. Many methods have been proposed, such as Rocchio method, Naive bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct a classifier. A new coming text document's category can be predicted. However, these methods do not give the description of each category. In the machine learning field, there are many concept learning algorithms, such as, ID3 and CN2. This paper proposes a more robust algorithm to induce concepts from training examples, which is based on enumeration of all possible keywords combinations. Experimental results show that the rules produced by our approach have more precision and simplicity than that of other methods.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder
ISBN 9780780390355
0780390350
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2005 IEEE.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005719

Document type: Conference Paper
Collections: School of Information Technology
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